LLM4DSLs: Unterschied zwischen den Versionen
(Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Nathan Hagel |email=nathan.hagel@student.kit.edu |vortragstyp=Masterarbeit |betreuer=Sebastian Weber |termin=Institutsseminar/2024-09-13 |vortragsmodus=in Präsenz |kurzfassung=Kurzfassung }}“) |
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|vortragender=Nathan Hagel | |vortragender=Nathan Hagel | ||
|email=nathan.hagel@student.kit.edu | |email=nathan.hagel@student.kit.edu | ||
|vortragssprache=Englisch | |||
|vortragstyp=Masterarbeit | |vortragstyp=Masterarbeit | ||
|betreuer=Sebastian Weber | |betreuer=Sebastian Weber | ||
|termin=Institutsseminar/2024-09-13 | |termin=Institutsseminar/2024-09-13 | ||
|vortragsmodus=in Präsenz | |vortragsmodus=in Präsenz | ||
|kurzfassung= | |kurzfassung=The steady rise of generative AI and large language models in software development raises the question of its applicability in model-driven engineering (MDE). | ||
Especially when using textual domain-specific languages (DSL) for modeling, insights into the domain itself and the internals of the DSL are required to create adequate models. | |||
To tackle this challenge, we present an updated MDE process aided by LLMs, which removes the necessity of manually creating textual models / writing DSL code manually. | |||
Additionally, we implemented tooling to use the updated process in practice. | |||
The process is evaluated in a user study and shows that it is faster and more efficient than traditional MDE. | |||
Furthermore, although not significantly better rated in perceived usability, 15 out of 18 participants still preferred the new process. | |||
Our results indicate that MDE is a good application for LLMs and can benefit from its capabilities. | |||
}} | }} | ||
Aktuelle Version vom 26. August 2024, 12:40 Uhr
| Vortragende(r) | Nathan Hagel | |
|---|---|---|
| Vortragstyp | Masterarbeit | |
| Betreuer(in) | Sebastian Weber | |
| Termin | Fr 13. September 2024 | |
| Vortragssprache | Englisch | |
| Vortragsmodus | in Präsenz | |
| Kurzfassung | The steady rise of generative AI and large language models in software development raises the question of its applicability in model-driven engineering (MDE).
Especially when using textual domain-specific languages (DSL) for modeling, insights into the domain itself and the internals of the DSL are required to create adequate models. To tackle this challenge, we present an updated MDE process aided by LLMs, which removes the necessity of manually creating textual models / writing DSL code manually. Additionally, we implemented tooling to use the updated process in practice. The process is evaluated in a user study and shows that it is faster and more efficient than traditional MDE. Furthermore, although not significantly better rated in perceived usability, 15 out of 18 participants still preferred the new process. Our results indicate that MDE is a good application for LLMs and can benefit from its capabilities. | |